Keyword index pruning

ABSTRACT

A system and method is disclosed for profiling a subject&#39;s search engine keywords and results based on relevancy feedback. Because the system is based on the search behavior of the user, the profiling is language independent and balances the specificity of search terms against the profiled interests of the user. The system can also score keywords on their search effectiveness and eliminate ineffective keywords from the keyword index. The system can also synthesize new keyword combinations to assist the user in refining the search or acquiring related content. The system has application in text mining, personalization, behavioral search, search engine optimization, and content acquisition, to name but a few applications.

FIELD OF THE INVENTION

The present invention relates to electronic search and retrieval of goods, services, content, and other entities through the automated analysis of human behavior. Particularly, the invention relates to the identification of meaningful and effective keywords and provides for eliminating ineffective keywords from a keyword index. The system has application in text mining, personalization, behavioral search, search engine optimization, and content acquisition, to name but a few applications.

BACKGROUND OF THE INVENTION

The consumer faces a profound number of possible choices when selecting most kinds of products, be it movies, music, books, travel, art, dining, employers, and so on, to the extent that the consumer must choose from well-publicized possibilities, such as through advertising, or rely on recommendations of others. In the first case the set of choices is severely limited to those that can be promoted to a broad audience. In the second case the consumer must weigh the similarity of his or her own tastes to the person making the recommendation, whether it be an acquaintance or media. In addition, the number of possibilities and the cost of acquisition, both in terms of time and money, of assessing possibilities, make it infeasible to sample a large number of possibilities to determine which are of interest to the consumer.

Recommendation systems rely on trying to best match a person's individual preferences to the characteristics of the available items. In general what is known about the subjects and objects is the set of affinities between subjects and objects, where the affinity {A_(ij)} between subject i and object j is determined by explicit feedback from the subject or inferred from the subject's interaction (or non-interaction) with the object. The consistency of the affinity scale from subject to subject and object to object derives from the consistency of the goal of the subjects in the given environment, for example to make a purchase in a commerce environment or to read articles in a content environment.

The primary goal of the recommendation system is to predict for a given subject those objects for which the subject will have the greatest affinity. In general the subject characteristics can be represented by a vector S=(S₁, S₂, . . . , S₁) and the object characteristics can be represented by a vector B=(B₁, B₂, . . . , B_(M)), whereby the predicted affinity of the subject to the object is a function P=f(S, B). Various recommendation systems then differ in their representation of subject and object characteristics S and B and the similarity function f.

One method that has been used, commonly referred to as collaborative filtering, is to represent the subject as the set of object ratings that the subject has provided; i.e., S={R₁, R₂, . . . , R_(L)}, where R_(i) is the subject's rating of object i. In most scenarios where recommendations are of use, the number of available items (e.g., such as catalog size) is going to be much larger than the number of items that have been rated by the subject, and so the set S is sparse. To generate a recommendation of a particular object to a particular subject, the subject's profile is compared to the profiles of other subjects that have rated the object. Given the similarities and dissimilarities of objects that have been rated in common, an estimate of the subject's response is generated. In a recommendation system, the system would generate estimates for a variety of objects rated by similar people and return as recommendations the objects with the highest predicted ratings.

Effectively, this type of system is a “mentored” system, whereby each subject is matched to a set of other subjects with similar tastes that have rated objects that the subject has not rated. This approach has several drawbacks, which include: recommendations can only be made where the subject's small set of mentors have provided coverage; the method is dependent on a class of users that have provided a large number of ratings (i.e., mentors), or else the system database must be searched to provide mentors appropriate to each requested object; the method is limited in capacity to make recommendations across various categories (cross marketing); the method does not make full use of all data for each subject (that is, the method is restricted to the subset of mentors); the representation of the subject, which is the set of ratings for each rated object, is not compact and increases linearly with the number of rated objects; subject representations are not portable to other recommendation systems; requires lengthy questionnaires to introduce a new subject into the system; and faces combinatorial challenges to find the best mentor for a given user and is therefore not scalable to large user populations.

Additional desired characteristics of a recommendation system that cannot be addressed by the mentor method include inverse modeling of subject representations back to physical attributes, such as demographics or psychographics, and identification and representation of object similarities.

Another approach is shopping basket analysis, which makes suggestions based on objects that have been purchased by other subjects at the same time as an object that has been selected by the targeted subject. However, this approach relies on transactional dependency and does not allow prediction of preference for objects that are not purchased together. In particular this method cannot associate subject/object affinities across catalog or across time as catalog items are replaced by similar items. Shopping basket analysis is also not specific to individual subjects, but rather to aggregate transaction histories across all subjects. By contrast, the present invention automatically normalizes all product profiles across product categories and can combine information across single vendor transaction histories.

Other approaches classify objects according to expert defined categories or attributes, whereby each object is rated by each attribute and then recommendations are made by matching the expressed interests of the subject to the attributes of the objects. Expert systems have the drawback that they are not self-adapting; that is, they require expert classification or coding. This means that such systems are specific to a single product domain. Also, because they are not data driven, they are not able to process large, diverse, and constantly changing transaction histories.

Predictive modeling techniques use demographics to model subjects. Not only are demographics an indirect substitute for aesthetic opinions and therefore inherently inaccurate, this is invasive of the subject's privacy and only specific to groups of subjects and not to individual subjects.

SUMMARY OF THE INVENTION

In accordance with the present invention, a method and system is presented for collecting subjects' affinities to objects, extracting subject and object profiles, and generating recommendations to subjects of objects that were rated by other subjects. This invention is sometimes referred to in this patent by its commercial trademarked name, Resonance®.

An object of the invention is to provide a means of recommending objects to subjects based on either explicit or behaviorally inferred ratings of other subjects of those objects and of commonly rated objects.

Another object of the invention is to compactly and uniformly represent subject and object profiles so that the affinity, or preference, of the subject to the object can be quickly and efficiently predicted, and so that the number of features in the profiles of the subjects and objects is not directly dependent on the number of subjects or objects in the system.

Another object of the invention is to create a representation of objects that is universal across all types of objects, so that all types of objects can be compared to one another and the subject's interaction with one set of objects can be extrapolated to other types of objects, and that the representation is derived solely from the collective interaction of subjects with the set of objects (catalog) and does not require detailed object information or expert knowledge of object characteristics.

Another object of the invention is to enable the use of all subject-object interactions, and not just, for example, purchase behavior, to form profiles of subject and objects for faster profiling and greater accuracy and responsiveness to temporal changes in site merchandising or customer behavior.

Another object of the invention is to create object profiles as well as subject profiles, so that objects can be readily indexed by aesthetic or other categories and so that objects can be readily associated across product categories by aesthetic similarity.

Another object of the invention is to create subject and object profiles that can be used to relate the derived aesthetic attributes to other objective measures of subjects, such as personality type or demographics, and objects, such as color or shape.

Another object of the invention is to collect ratings information from multiple applications while protecting the anonymity of the subject across different applications and minimizing the need to normalize object information (metadata) across catalogs.

Another object of the invention is to combine the recommendations of the system with explicit human merchandising objectives either through “hard” rules that filter results by specified criteria or “soft” rules that bias the results towards a defined business goal.

Another object of the invention is to provide recommendations to groups of subjects based on the best match to their collective profiles.

Another object of the invention is to enhance product and content marketing by characterizing the attributes of object profiles.

Another object of the invention is to identify appropriate subjects for the marketing of a particular object.

The present invention is a system and method for predicting subject responses to objects based on other subjects' responses to that and other objects. The process of matching subject and object profiles produces a predicted response score that can be used to rank recommended content. The scores can be used as is or combined with other business logic to render the final recommendation rank. The invention can be applied to a broad range of applications, including the retailing of single consumption items, such as non-recurring purchases or content views, where the previous purchase or view of an object cannot be used to predict additional purchases or views of the same object. The invention can also be used to predict subject responses to recurring purchases and to recommend new consumables.

The invention considers the interaction of subjects and objects. The subject is an active entity that initiates transactions. The subject consumes or experiences objects and provides feedback on the level of satisfaction with the object. The subject could be a single person or a corporate entity, such as a business. The object is a passive target of interaction by the subject. This could be a physical object, such as a consumer good, for example cars, MP3 player, or ice cream; media, such as music, movies, books, art, or plays; or even a person, as in the case of a job search or a matchmaking service. In the case of active entities, it is possible for the subject and object to reverse roles depending on the situation.

The invention provides a novel solution to the problem of how to identify objects, for example products, that will appeal to a particular subject, for example a person, where the large number of possible objects, including less desirable objects that are descriptively similar but aesthetically different or where some objects may appeal highly to a limited population of subjects while being undesirable to the broader population, makes it difficult for the subject to notice the objects that the subject wants simply by browsing the entire set of objects. This provides a breakthrough for target marketing and retail applications because it allows the consumer, solely by behavior, to “self-market” or “pull” those products which are of interest, rather than requiring that retailers “push” potentially unwanted products through advertising or other inefficient means.

The invention also addresses the issue of consumer privacy because it does not profile the consumer using personal demographics information, which consumers find both invasive and tedious to enter. Thus Resonance improves retailers' ability to target customers, while simultaneously making it easier for consumers to participate.

The invention works by forming profiles of subjects, for example consumers, and objects, such as goods or media, based on aesthetic evaluations of objects by subjects. The invention does not require a priori information about either subjects, such as demographics or psychographics, or objects, such as classifications or genres. Rather, it automatically generates representations of subjects and objects solely from the subjects' interaction with the objects. Because it creates its own abstract representation of subjects, it allows retailers to transparently target the subject without compromising subject privacy through the collection and modeling of sensitive personal information. The profiles can also be extended across catalogs, product or content domains, or across websites or stores.

Note that the identification of subjects and objects is not a physical one and may change depending on the application. For example, in a consumer movie recommendation application, the person requesting recommendations is the subject and the movie is the object. In a dating service application, a person would be considered a subject when searching for matches and an object when being searched by others. Similarly, in the case of employer/employee matching, companies and persons would alternate between the roles of subject and object. Note that in cases where an entity can assume different roles, a different profile would be created for each role.

Because the profiles are symmetric (both subjects and objects are profiled to the same representation), subjects can be matched to other subjects or objects, and objects can be matched to other objects or subjects. For example subject-subject matching could be used on a social networking site to connect people of like interests or on an online store to order product reviews according to the similarity of the reviewer to the reader. Similarly, object-object matching can be used to match keywords to products or content, advertisements to news articles, or promotional banners to referring affiliate sites.

Subjects and objects are represented as a set of derived abstract attributes, or feature vectors. In addition to driving the matching process, the distributions of the dimensions can be used to predict which items will evoke strong reactions (negative and positive) from a typical group of subjects and which items will evoke a more even response across those subjects. The invention also relates to the field of neural networks and optimization. Generally, neural networks take an input vector through a transfer function to produce an output. Neural network training methods are classified into supervised and unsupervised models. In supervised models the training method adapts the weights of the transfer function to minimize some function of the outputs, such as the mean squared error between the outputs produced by the transformation of the inputs and the expected outputs, which are known for a certain set of inputs commonly known as the training set. Once the weights have been trained, the network can be used to predict outputs for operational inputs. In unsupervised networks the data is clustered in some way that makes it useful for subsequent processing; however, the desired result, namely the closest matching template, is not known during training.

The proposed method is supervised insofar as the desired outputs are known as part of a training set; however, similar to unsupervised methods, the method is also self-organizing insofar as the inputs are not known; i.e., the method derives the input values using a known transfer function and known outputs.

Because of the large number of weights or adapted parameters of the system, which scales as the number of subjects and objects, a key aspect of the method is that the weights for each subject or object are decoupled from other subjects and objects when updated separately. This allows individual subjects and objects to be trained by different processing units, which allows the method to scale up to large numbers of subjects and objects, which may ultimately total millions or tens of millions or more.

A key improvement of the invention over other collaborative filters is that it creates not just profiles of the subjects, but profiles of the objects as well. This provides several advantages, including rapid and scalable prediction of subject to object affinities; straightforward cross marketing across product categories; and sorting of objects by aesthetic categories for purposes of browsing and selecting items for consumption or association, such as selecting musical recordings to go with a movie production.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts examples of relationships between the subject and various types of individualized content and other subjects.

FIG. 2 illustrates the operation of the system to match and rank one type of object against a set of objects of another type.

FIG. 3 depicts the operation of the system as a search engine.

FIG. 4 depicts the operation of the system as a search engine using implicit feedback based on subject response to search results.

FIG. 5 illustrates the operation of the behavioral engine to eliminate low information tokens from keyword phrases.

FIG. 6 illustrates the operation of the behavioral engine as it pertains to computing the token score check shown in FIG. 5.

FIG. 7 depicts the operation of the system as a content acquisition engine.

FIG. 8 depicts the content acquisition workflow.

DETAILED DESCRIPTION OF THE DRAWINGS

The following detailed description is related to the technology disclosed in U.S. patent application Ser. No. 12/415,758 filed on Mar. 31, 2009 and U.S. patent application Ser. No. 12/416,005 filed on Mar. 31, 2009, and both are incorporated by reference herein, in their entirety. FIG. 1 shows examples of relationships between the subject and various types of individualized content and other subjects. As shown, the system simultaneously profiles any content that can be individually and repeatably identified by the system, using the systems and methods described previously in U.S. patent application Ser. No. 12/415,758 and U.S. patent application Ser. No. 12/416,005. Associations are created between subjects and different object types as subjects interact with multiple types of objects within a session. Once these associations are created, the profiler develops universal profiles across object types, so that any object can be matched against any subject or any other object, regardless of type.

FIG. 2 shows the operation of the system to match and rank one type of object against a set of objects of another type. This is similar to the method described previously in U.S. patent application Ser. No. 12/415,758 filed on Mar. 31, 2009 and U.S. patent application Ser. No. 12/416,005 filed on Mar. 31, 2009, except that the target objects are restricted to those that are of the type of object being requested, for example text, products, keywords, blogs, reviews, news, e-mail, multimedia, or referring/source links. For the embodiment of the system as a search engine, the source entity is a keyword, or search term. Note that the matching and targeting can also be done using a subject as the source entity, and that the source entity, either subject or object, can be matched against any subjects as well. The recommendation request is handled as follows: First, the source object vector is retrieved from the source object catalog. Next, the set of target object vectors are retrieved from the target object catalog and matched/scored against the source object vector. The scored objects are ranked and the top-ranking objects are returned to the application for presentation to the user.

FIG. 3 shows the operation of the system as a search engine. In this application the keywords are processed by both a conventional text-based search engine, such as Google, and a behavioral search engine that utilizes the behavioral profiling and targeting system described in this invention. The text-based search engine generates a relevancy score for each target object. The behavioral search engine matches the keyword profiles and the target object profiles to produce a behavioral score for each target object. If the keyword has not been profiled, then the individual user profile is matched against the object profiles to produce the behavioral scores for the target objects. Note that in the latter case, the behavior combines both the general relevancy against the keyword as well as the specific interests of the user.

Alternatively, this can be made more explicit by creating the behavior score as a combination of the scores that result from matching keyword objects with the target objects and the scores that result from matching the subject with the target objects. Regardless of how the behavior score is derived, the relevancy scores and the behavioral scores are combined according to the convex combination: COMBINED SCORE=a*(TEXT SCORE)+(1−a)*(BEHAVIOR SCORE), where a is a continuous value between 0 and 1, inclusive, that determines the weighting or balance between the two search engines. The search results are then ranked by the combined score, and the highest-ranking results are presented back to the user. Note that this method of combining scores naturally balances which engine drives the search results back to the user. For example, a very specific search might produce a sharp falloff in relevancy scores, in which case the behavioral scores would only slightly modify the ranking of the top search results, whereas a more general search might produce a broad distribution of relevancy scores, so that the behavioral scores and the additional inferred interests of the user become the differentiators in ranking the search results.

Once the search results are presented to the user, the user then provides feedback on the relevancy of each given result based on either direct explicit rating of the relevancy, such as by rating a given item as “Relevant” or “Not Relevant”, or implicit rating through selection, non-selection, and subsequent action. The subject, keyword object, result objects, result objects' relevancy scores, and relevancy score weights are then submitted to the profiler, which updates the subject, keyword object, and result object profiles according to the manner described previously in U.S. patent application Ser. No. 12/415,758 filed on Mar. 31, 2009 and U.S. patent application Ser. No. 12/416,005 filed on Mar. 31, 2009. Note that because the profiling is based on the user's response to their keyword searches, the system does not need to parse or otherwise analyze the keywords or the target objects, so that in particular the system is language-independent, in fact provides a de facto translation of keywords as different language users perform searches for similar content.

Also, the system does not require a pre-built taxonomy to provide targeted search results even in expert domains. Also note that in building the keyword profiles, if the search is performed in a domain where the expertise of the user is important in determining the relevancy of a search result, such as in the case of a technical, medical, or other highly targeted search domain, the relevancy scores from designated experts may be weighted more than those of casual or less-expert users. Also note that when presented with an unknown keyword phrase, the behavior engine can bootstrap itself by presenting random results and then learning from user response, but it is more efficient and desirable to have the system default to a text engine in these circumstances. Alternatively, the system can break the phrase into recognizable components, for example individual words that have already been profiled by the system, and then combine the results of the individual components together according to the method of Circle of Friends described previously in U.S. patent application Ser. No. 12/415,758 filed on Mar. 31, 2009.

FIG. 4 shows the operation of the system as a search engine using implicit feedback based on subject response to search results. This sample embodiment would apply when the Resonance behavioral engine is used in a non-expert, general search environment. In such cases, the relevancy scores and weighting are derived using a target model as described previously in U.S. patent application Ser. No. 12/416,005 filed on Mar. 31, 2009. In this model, results that are presented and not selected by the user for further investigation are considered low-relevance and low-weight. If a result is presented multiple times to the user and not selected, then that increases the weighting of that negative response. Results that are selected are considered higher relevance and higher weight. Of the selected results further relevance can be attributed based on re-selecting a search result or spending more time examining the contents of a search result.

FIG. 5 shows the operation of the behavioral engine in FIG. 4 in greater detail. This sample embodiment illustrates how the system handles keywords, or in the case of keyword phrases their recognizable components, for example individual words, whose behavioral profile does not support a robust match against target objects. A keyword phrase is broken into tokens, for example words, separated by whitespace or other separator. For each token, the behavioral engine matches the token profile and the target object profiles to produce a behavioral score for each target object. The set of behavioral scores corresponding to a token is checked to see if they are sufficient to differentiate high relevance results from low relevance results. This is called the token score check. Each token is considered separately. If the behavioral engine does not have a profile for a token, then the token is skipped. For each token that passes the token score check, the corresponding target object behavioral scores are combined to form a final set of behavioral scores. If no token passes the token score check, the engine treats the keyword phrase as if it had not been profiled, as described above. Optionally, for each token that fails the token score check, the system can delete the corresponding profile in order to save space and speed future processing. Another benefit of deleting the profile associated with a token that failed the token score check is the engine can regenerate the profile based on new behavioral data, refreshing the profile of a token whose past behavioral profile has degraded to the point of lacking sufficient predictive power. Note that by eliminating tokens that do not support a robust match to target objects, the behavioral engine generates more effective matches without explicit or expert knowledge of the language or the alphabet of the keyword phrases. Also note that this information theoretic approach to improving the effectiveness of the behavioral engine does not rely on any explicit modeling of the space of keywords or their statistical properties.

FIG. 6 shows the operation of the behavioral engine as it pertains to performing the token score check illustrated in FIG. 5. The input to the token score check is a set of behavioral scores corresponding to target objects. If every behavioral score falls below a given threshold, the check fails. If at least one behavioral score passes a given threshold, then the behavioral scores are quantized into a discrete set of score bins x_(i), where x_(i) denotes a contiguous set of behavioral scores, x_(i) is disjoint from x_(j) when i≠j, and the union of x_(i) over all i is the set of all behavioral scores. The engine then evaluates whether the entropy H of the distribution of behavioral scores is above a certain threshold. Entropy is defined as

$H = {- {\sum\limits_{i = 1}^{n}{{p\left( x_{i} \right)}\log\;{p\left( x_{i} \right)}}}}$ where p(x_(i)) denotes the probability that the behavioral score of a particular target object is in the set x_(i). High entropy indicates that the scores for all target objects are similar, and therefore do not effectively differentiate which target objects are good matches for a given token. If H is above a certain threshold, the token score check fails. Note that the entropy of distribution of behavioral scores is independent of the magnitude of the scores. Therefore, to pass the token score check, a set of scores must not only contains scores that are sufficiently large to indicate high relevance, but must also be sufficiently skewed in distribution to differentiate high relevance results from low relevance results. If the profile for a token is well matched with all target objects, then the token score check can still fail, since a set of uniformly large behavioral scores does not aid the engine in differentiating between high relevance target objects and low relevance target objects.

FIG. 7 shows the operation of the system as a content acquisition engine. There is a search agent who is requesting and evaluating results through interaction with a search result viewer. The search agent generates relevancy feedback through interaction with the results in the results viewer. These are used by the profiler to create profiles of the search agents, keywords, and result objects. These profiles are then matched by the keyword synthesizer against other profiled keywords to create new trial keyword phrases.

FIG. 8 shows the content acquisition workflow. First, an initiating keyword phrase is submitted by the search user to an external search engine, which then returns search results for review by the search user. The user then implicitly or explicitly scores the relevancy of the results. The system profiles the search user, keywords, and results based on the search interests and the associated responses of keywords and results within a search session. After eliminating the keyword tokens that do not meet the effectiveness criteria as described above, the profiles are then used to generate new keywords by matching the search user subject profile to the keyword profiles and then using the top-matching keyword phrase and/or individual keyword components to generate new trial keywords for subsequent search. This process is iterated and refined as the search user provides feedback on the subsequent search results. In addition, the object vector clustering techniques described previously in U.S. patent application Ser. No. 12/415,758 filed on Mar. 31, 2009 can be used to tag new content of interest as it comes into the system by setting a region of interest around a keyword profile vector. The user can adjust how much content is tagged by adjusting the size of the region of interest.

The above application embodiments are for example only. The invention does not restrict the design and implementation of the application in any way, as long as the interface to the recommendation engine matches the protocols defined by the interface guidelines. A key aspect of the invention is that, unlike other methods, it does not in fact require knowledge of the application search domain, does not need to parse the application's content to extract metadata to associate with keywords, and does not need taxonomic or semantic information about the application search domain, so that the system is language-independent and self-adapts to the interests of each user.

The foregoing description of a preferred embodiment has been presented by way of example only, and should not be read in a limiting sense. The scope of the present invention is defined only by the following claims.

In the claims reference characters used to designate claim steps are provided for convenience of description only, and are not intended to imply a particular order for performing the steps. 

What is claimed is:
 1. A computer implemented web-based system for personalized search using keyword index pruning, comprising: a computer implemented web-based service system, including a memory component and a processor component, for searching, collecting relevancy feedback, and acquiring content by keywords; wherein profiles of subjects, including search users, are stored in a subject profile database and profiles of objects, including keyword and target content items, are stored in an object catalog databases; said subject and keyword profiles are matched against target profiles and ranked to be optimal to keywords; wherein optimal subject and object profiles are derived from said subject's relevancy ratings of search result objects against inputted keywords; and further wherein said web-based service system processes, identifies and eliminates ineffective keywords by performing a token score check by operation of a behavioral engine wherein the input to the token score check is a set of behavioral scores corresponding to target objects and the behavioral scores are quantized into a discrete set of score bins x_(i), where x_(i) denotes a contiguous set of behavioral scores, and further wherein the effectiveness of the keyword is measured by the entropy H of the distribution of behavioral scores; whereby said entropy is defined as $H = {- {\sum\limits_{i = 1}^{n}{{p\left( x_{i} \right)}\log\;{p\left( x_{i} \right)}}}}$ where p(x_(i)) denotes the probability that the behavioral score of a particular target object is in the set x_(i).
 2. The web-based system for personalized search using keyword index pruning, according to claim 1, wherein said subject and keyword profiles are matched against target profiles and ranked to be optimal to keywords further includes a target object catalog database and matching and ranking of one subject or object type against a set of target objects of another type derived from said target object catalog database.
 3. The web-based system for personalized search using keyword index pruning, according to claim 2, wherein said target objects are restricted to those that are of the same type of object being requested in the keyword search.
 4. The web-based system for personalized search using keyword index pruning, according to claim 1, wherein said subject and keyword profiles are matched against target profiles and ranked to be optimal to keywords further includes combining the matching scores of the keyword profiles against the target object catalog with the matching scores of the subject profiles against the same target object catalog, whereby the search results are then ranked by the combined score.
 5. The web-based system for personalized search using keyword index pruning, according to claim 4, wherein said matching scores of the keyword profiles against the target object catalog includes breaking keyword phases into one or more keyword token objects, whereby the matching score of the keyword phrase against a target object is a combination of the matching scores of the individual keyword tokens.
 6. The web-based system for personalized search using keyword index pruning, according to claim 5, wherein said breaking keyword phases into one or more keyword token objects and matching score of the keyword phrase to a target object is a combination of the matching scores of the individual keyword tokens includes: each token is considered separately; for each token, the behavioral engine matches the token profile and the target object profiles to produce a behavioral score for each target object; and the behavioral scores corresponding to a token are checked to see if they are sufficient to differentiate high relevance results from low relevance results; whereby if the behavioral engine does not have a profile for a token, then the token is skipped, and for each token that passes the token score check, the corresponding target object behavioral scores are combined to form a final set of behavioral scores.
 7. The web-based system for personalized search using keyword index pruning, according to claim 1, wherein said keywords are processed by both a conventional text-based search engine, and a behavioral search engine that utilizes a behavioral profiling and targeting system whereby a text-based search engine generates a relevancy score for each target object; a behavioral search engine matches the subject and keyword profiles and the target object profiles to produce a behavioral score for each target object.
 8. The web-based system for personalized search using keyword index pruning, according to claim 7, wherein said behavior score is calculated as a combination of the scores that result from matching keyword objects with the target objects and the scores that result from matching the subject profile to the target objects; whereby said behavior score is derived, and the relevancy scores and the behavioral scores are combined according to the convex combination: COMBINED SCORE=a*(TEXT SCORE)+(1−a)*(BEHAVIOR SCORE), where a is a continuous value between 0 and 1, inclusive, that determines the weighting or balance between the two search engines.
 9. The web-based system for personalized search using keyword index pruning, according to claim 1, wherein said web-based system for personalized search using keyword index pruning is language-independent, whereby said system does not parse or analyze the keywords or the target objects and provides a translation of keywords as an equivalence of search response as different language users perform searches for similar content.
 10. The web-based system for personalized search using keyword index pruning, according to claim 1, further comprising a behavior engine wherein when presented with an unknown keyword phrase, said behavior engine can bootstrap itself by presenting random results and then learning from a user response.
 11. The web-based system for personalized search using keyword index pruning, according to claim 1, further comprising a behavior engine wherein when presented with an unknown keyword phrase, said behavior engine can bootstrap itself by presenting results from matching the subject profile to the target object catalog and then learning from a user response.
 12. The web-based system for personalized search using keyword index pruning, according to claim 1, further comprising when a behavioral engine is used in a non-expert, general search environment: the relevancy scores and weighting are derived using a target model based on user response to search result objects; results that are presented and not selected by the user for further investigation are considered low-relevance and low-weight; if a result is presented multiple times to the user and not selected, then that increases the weighting of that negative response; and results that are selected are considered higher relevance and higher weight whereby of the selected results further relevance can be attributed based on re-selecting a search result or spending more time examining the contents of a search result.
 13. The web-based system for personalized search using keyword index pruning, according to claim 1, further comprising the behavioral engine then evaluating whether the entropy H of the distribution of behavioral scores is above a certain threshold, thereby indicating lack of search discrimination; and eliminating keywords from the keyword profile index database that are greater than such entropy threshold.
 14. The web-based system for personalized search using keyword index pruning, according to claim 1, further comprising operating the system as a content acquisition engine wherein a search user utilizes the system to: request and evaluate search results through interaction with a search result viewer; generate relevancy feedback through interaction with the results in the results viewer; generated relevancy feedback is used by the profiler to create profiles of the search users, keywords, and result objects; and said profiles are then matched by the keyword synthesizer against other profiled keywords to create new trial keyword phrases.
 15. The web-based system for personalized search using keyword index pruning, according to claim 14, further comprising said system configured to: submit an initiating keyword phrase by the search user to an external search engine, which then returns search results for review by the search user; collect from the user implicit or explicit scores based upon the relevancy of the results; profile the search user, keywords, and results based on the search interests and the associated responses of keywords and results; eliminate the keyword tokens that do not meet the effectiveness criteria; generate new keywords by matching the search user subject profile to the keyword profiles; wherein the top-matching keyword tokens to generate new trial keyword phrases for subsequent search; and the process is iterated and refined as the search user provides feedback on the subsequent search results.
 16. The web-based system for personalized search using keyword index pruning, according to claim 15, wherein subject and keyword profiles are represented as vectors, whereby the matching of a subject or keyword vector to a target object vector generates a relevancy score.
 17. The web-based system for personalized search using keyword index pruning, according to claim 16, further wherein new content of interest is tagged as it comes into the system by setting a region of interest around a keyword profile vector.
 18. The web-based system for personalized search using keyword index pruning, according to claim 17, further wherein the user can adjust how much content is tagged by adjusting the size of the region of interest. 